期刊
IEEE NETWORK
卷 36, 期 4, 页码 70-77出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MNET.005.2100687
关键词
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This study proposes a flexible and adaptive resource management framework for addressing the challenges posed by harsh environments on networking strategies. The framework incorporates environment awareness functionality and introduces a new network architecture with deep-learning-based resource prediction and self-organized service management modules. Experimental results demonstrate the effectiveness and efficiency of the proposed functionalities. Several promising directions for resource management in harsh environments are also highlighted.
The harsh environment imposes a unique set of challenges on networking strategies. In such circumstances, the environmental impact on network resources and long-time unattended maintenance has not been well investigated yet. To address these challenges, we propose a flexible and adaptive resource management framework that incorporates environment awareness functionality. In particular, we propose a new network architecture and introduce the new functionalities against the traditional network components. The novelties of the proposed architecture include a deep-learning-based environment resource prediction module and a self-organized service management module. Specifically, the available network resource under various environmental conditions is predicted by using the prediction module. Then, based on the prediction, an environment-oriented resource allocation method is developed to optimize the system utility. To demonstrate the effectiveness and efficiency of the proposed new functionalities, we examine the method via an experiment in a case study. Finally, we introduce several promising directions of resource management in harsh environments that can be extended from this article.
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